Abstract
Aiming at the shortcomings of the seagull optimization algorithm in the iterative process, such as slow convergence speed and easy to fall into local optimum, an improved seagull optimization (CNSOA) algorithm based on nonlinear convergence factor and mutation using the Cauchy operator is proposed. The tent chaotic mapping strategy is used to initialize the population that make the seagull population more uniformly distributed in the search space. In the process of seagull migration, a nonlinear convergence factor is used to guide the seagull to seek optimization, so that the algorithm has better search ability. The Cauchy mutation perturbation strategy is adopted to make the algorithm better jump out of the local optimum. Finally, 9 benchmark test functions are used to test the CNSOA, and the results are compared with the SOA and 5 famous algorithms. The experimental results show that the CNSOA performs better in convergence speed and jumping out of the local optimum.
Published Version
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